Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C137776N
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C137776N
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 9 clinical features across 503 patients, 37 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'PATHOLOGY.M.STAGE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.M.STAGE', and 'GENDER'.

  • CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.N.STAGE', and 'PATHOLOGY.M.STAGE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'PATHOLOGY.M.STAGE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.M.STAGE', and 'GENDER'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.N.STAGE',  'PATHOLOGY.M.STAGE', and 'GENDER'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'PATHOLOGY.M.STAGE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death' and 'GENDER'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death' and 'PATHOLOGY.M.STAGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 37 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE NEOPLASM
DISEASESTAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		GENDER KARNOFSKY
PERFORMANCE
SCORE 		NUMBERPACKYEARSSMOKED
Statistical Tests logrank test ANOVA Chi-square test Chi-square test Fisher's exact test Fisher's exact test Fisher's exact test ANOVA ANOVA
mRNA CNMF subtypes 0.131
(1.00) 		0.795
(1.00) 		0.00527
(0.295) 		0.00911
(0.437) 		0.0704
(1.00) 		0.12
(1.00) 		0.634
(1.00)
mRNA cHierClus subtypes 0.414
(1.00) 		0.651
(1.00) 		0.00728
(0.371) 		0.0125
(0.576) 		0.124
(1.00) 		0.104
(1.00) 		0.68
(1.00)
Copy Number Ratio CNMF subtypes 0.000342
(0.0236) 		0.00464
(0.264) 		8.78e-05
(0.00649) 		0.00148
(0.0888) 		0.0078
(0.39) 		4.72e-05
(0.00364) 		0.00651
(0.339) 		0.413
(1.00)
METHLYATION CNMF 2.05e-06
(0.000164) 		0.0063
(0.334) 		1.47e-11
(1.37e-09) 		5.4e-12
(5.08e-10) 		0.169
(1.00) 		0.000759
(0.0494) 		0.000957
(0.0603) 		0.595
(1.00)
RPPA CNMF subtypes 2.61e-10
(2.35e-08) 		0.129
(1.00) 		1.2e-08
(1.07e-06) 		9.52e-07
(7.81e-05) 		0.00106
(0.066) 		6.21e-07
(5.16e-05) 		0.0611
(1.00) 		0.322
(1.00)
RPPA cHierClus subtypes 1.29e-07
(1.12e-05) 		0.00927
(0.437) 		2e-06
(0.000162) 		4.03e-07
(3.39e-05) 		0.241
(1.00) 		0.000108
(0.00788) 		0.571
(1.00) 		0.0463
(1.00)
RNAseq CNMF subtypes 2.39e-07
(2.06e-05) 		0.115
(1.00) 		2.61e-07
(2.22e-05) 		3.83e-06
(0.000302) 		0.0131
(0.588) 		0.0011
(0.0669) 		4.91e-05
(0.00373) 		0.656
(1.00)
RNAseq cHierClus subtypes 1.23e-08
(1.08e-06) 		0.121
(1.00) 		1.75e-11
(1.61e-09) 		9.44e-11
(8.59e-09) 		0.00285
(0.168) 		5.51e-05
(0.00414) 		0.00013
(0.00936) 		0.456
(1.00)
MIRSEQ CNMF 4.49e-06
(0.00035) 		0.0386
(1.00) 		0.000131
(0.00936) 		0.000363
(0.0247) 		0.0057
(0.308) 		0.000328
(0.023) 		0.0056
(0.308) 		0.149
(1.00)
MIRSEQ CHIERARCHICAL 0.0162
(0.698) 		0.0418
(1.00) 		0.0967
(1.00) 		0.00808
(0.396) 		0.0231
(0.9) 		0.685
(1.00) 		0.648
(1.00) 		0.878
(1.00)
MIRseq Mature CNMF subtypes 0.000565
(0.0375) 		0.258
(1.00) 		0.05
(1.00) 		0.0178
(0.731) 		0.177
(1.00) 		0.105
(1.00) 		0.000559
(0.0375) 		0.288
(1.00)
MIRseq Mature cHierClus subtypes 0.000808
(0.0517) 		0.328
(1.00) 		0.0158
(0.694) 		0.0178
(0.731) 		0.655
(1.00) 		0.00295
(0.171) 		0.0164
(0.698) 		0.272
(1.00)
Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1.  Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 34 24 14
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.131 (logrank test), Q value = 1

Table S2.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 71 14 0.5 - 101.1 (34.3)
subtype1 33 4 0.6 - 101.1 (31.0)
subtype2 24 9 0.5 - 93.3 (38.1)
subtype3 14 1 1.3 - 84.4 (30.7)

Figure S1.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.795 (ANOVA), Q value = 1

Table S3.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 71 60.5 (12.4)
subtype1 33 60.2 (13.8)
subtype2 24 59.9 (11.1)
subtype3 14 62.6 (11.3)

Figure S2.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00527 (Chi-square test), Q value = 0.3

Table S4.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 40 13 14 5
subtype1 23 4 6 1
subtype2 9 3 8 4
subtype3 8 6 0 0

Figure S3.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00911 (Chi-square test), Q value = 0.44

Table S5.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3
ALL 41 14 17
subtype1 23 4 7
subtype2 10 4 10
subtype3 8 6 0

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0704 (Fisher's exact test), Q value = 1

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 35 3
subtype1 18 0
subtype2 10 3
subtype3 7 0

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.12 (Fisher's exact test), Q value = 1

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 67 5
subtype1 33 1
subtype2 20 4
subtype3 14 0

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.634 (Fisher's exact test), Q value = 1

Table S8.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 29 43
subtype1 15 19
subtype2 10 14
subtype3 4 10

Figure S7.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S9.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 15 33 24
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.414 (logrank test), Q value = 1

Table S10.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 71 14 0.5 - 101.1 (34.3)
subtype1 15 2 1.3 - 84.4 (27.2)
subtype2 32 4 0.6 - 101.1 (30.5)
subtype3 24 8 0.5 - 93.3 (38.1)

Figure S8.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.651 (ANOVA), Q value = 1

Table S11.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 71 60.5 (12.4)
subtype1 15 63.2 (11.2)
subtype2 32 59.9 (14.0)
subtype3 24 59.7 (10.9)

Figure S9.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00728 (Chi-square test), Q value = 0.37

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 40 13 14 5
subtype1 9 6 0 0
subtype2 22 4 6 1
subtype3 9 3 8 4

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0125 (Chi-square test), Q value = 0.58

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3
ALL 41 14 17
subtype1 9 6 0
subtype2 22 4 7
subtype3 10 4 10

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.124 (Fisher's exact test), Q value = 1

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 35 3
subtype1 7 0
subtype2 17 0
subtype3 11 3

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.104 (Fisher's exact test), Q value = 1

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 67 5
subtype1 15 0
subtype2 32 1
subtype3 20 4

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.68 (Fisher's exact test), Q value = 1

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 29 43
subtype1 5 10
subtype2 15 18
subtype3 9 15

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S17.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 167 208 119
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.000342 (logrank test), Q value = 0.024

Table S18.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 493 163 0.1 - 120.6 (36.8)
subtype1 167 73 0.2 - 109.9 (34.3)
subtype2 207 48 0.2 - 120.6 (37.2)
subtype3 119 42 0.1 - 102.4 (36.5)

Figure S15.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.00464 (ANOVA), Q value = 0.26

Table S19.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 493 60.6 (12.2)
subtype1 166 63.0 (11.7)
subtype2 208 59.6 (12.5)
subtype3 119 58.8 (11.9)

Figure S16.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 8.78e-05 (Chi-square test), Q value = 0.0065

Table S20.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 237 54 125 78
subtype1 62 18 47 40
subtype2 121 25 47 15
subtype3 54 11 31 23

Figure S17.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00148 (Chi-square test), Q value = 0.089

Table S21.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 242 65 176 11
subtype1 66 22 75 4
subtype2 121 28 58 1
subtype3 55 15 43 6

Figure S18.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0078 (Fisher's exact test), Q value = 0.39

Table S22.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 228 18
subtype1 71 11
subtype2 100 2
subtype3 57 5

Figure S19.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 4.72e-05 (Fisher's exact test), Q value = 0.0036

Table S23.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 417 77
subtype1 127 40
subtype2 192 16
subtype3 98 21

Figure S20.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00651 (Fisher's exact test), Q value = 0.34

Table S24.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 171 323
subtype1 43 124
subtype2 86 122
subtype3 42 77

Figure S21.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.413 (ANOVA), Q value = 1

Table S25.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 18 83.3 (30.9)
subtype2 11 91.8 (9.8)
subtype3 7 95.7 (7.9)

Figure S22.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S26.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 97 114 74
'METHLYATION CNMF' versus 'Time to Death'

P value = 2.05e-06 (logrank test), Q value = 0.00016

Table S27.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 285 95 0.2 - 120.6 (29.9)
subtype1 97 49 0.6 - 90.1 (28.8)
subtype2 114 18 0.2 - 120.6 (35.7)
subtype3 74 28 0.2 - 109.9 (26.5)

Figure S23.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0063 (ANOVA), Q value = 0.33

Table S28.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 285 61.5 (11.9)
subtype1 97 63.9 (10.5)
subtype2 114 58.9 (12.8)
subtype3 74 62.5 (11.7)

Figure S24.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.47e-11 (Chi-square test), Q value = 1.4e-09

Table S29.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 130 28 73 54
subtype1 19 9 39 30
subtype2 76 16 12 10
subtype3 35 3 22 14

Figure S25.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 5.4e-12 (Chi-square test), Q value = 5.1e-10

Table S30.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 133 37 107 8
subtype1 20 14 60 3
subtype2 76 19 19 0
subtype3 37 4 28 5

Figure S26.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.169 (Fisher's exact test), Q value = 1

Table S31.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 127 9
subtype1 41 5
subtype2 51 1
subtype3 35 3

Figure S27.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.000759 (Fisher's exact test), Q value = 0.049

Table S32.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 233 52
subtype1 68 29
subtype2 103 11
subtype3 62 12

Figure S28.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.000957 (Fisher's exact test), Q value = 0.06

Table S33.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 96 189
subtype1 20 77
subtype2 51 63
subtype3 25 49

Figure S29.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.595 (ANOVA), Q value = 1

Table S34.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 28 92.5 (8.0)
subtype1 6 91.7 (7.5)
subtype2 14 91.4 (8.6)
subtype3 8 95.0 (7.6)

Figure S30.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S35.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 101 90 86 76 44 57
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 2.61e-10 (logrank test), Q value = 2.4e-08

Table S36.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 454 156 0.1 - 120.6 (36.4)
subtype1 101 27 0.2 - 120.6 (46.5)
subtype2 90 35 0.1 - 90.4 (31.0)
subtype3 86 23 0.2 - 112.8 (35.8)
subtype4 76 24 0.8 - 99.8 (37.1)
subtype5 44 8 0.9 - 83.8 (36.7)
subtype6 57 39 0.6 - 102.4 (20.9)

Figure S31.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.129 (ANOVA), Q value = 1

Table S37.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 453 60.4 (12.3)
subtype1 100 61.8 (11.1)
subtype2 90 58.0 (12.2)
subtype3 86 62.3 (12.6)
subtype4 76 60.0 (11.8)
subtype5 44 58.3 (15.7)
subtype6 57 61.2 (11.4)

Figure S32.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.2e-08 (Chi-square test), Q value = 1.1e-06

Table S38.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 219 44 115 76
subtype1 54 11 22 14
subtype2 42 9 19 20
subtype3 39 12 24 11
subtype4 44 7 17 8
subtype5 33 2 9 0
subtype6 7 3 24 23

Figure S33.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 9.52e-07 (Chi-square test), Q value = 7.8e-05

Table S39.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 224 54 165 11
subtype1 54 15 31 1
subtype2 43 10 32 5
subtype3 41 12 33 0
subtype4 45 7 23 1
subtype5 33 2 9 0
subtype6 8 8 37 4

Figure S34.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.00106 (Chi-square test), Q value = 0.066

Table S40.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 208 16
subtype1 48 1
subtype2 46 2
subtype3 43 2
subtype4 35 3
subtype5 13 0
subtype6 23 8

Figure S35.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 6.21e-07 (Chi-square test), Q value = 5.2e-05

Table S41.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 379 75
subtype1 87 14
subtype2 71 19
subtype3 75 11
subtype4 68 8
subtype5 44 0
subtype6 34 23

Figure S36.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0611 (Chi-square test), Q value = 1

Table S42.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 44 57
subtype2 29 61
subtype3 27 59
subtype4 18 58
subtype5 18 26
subtype6 15 42

Figure S37.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.322 (ANOVA), Q value = 1

Table S43.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 93.5 (7.7)
subtype1 7 92.9 (7.6)
subtype2 8 93.8 (5.2)
subtype3 10 90.0 (9.4)
subtype4 2 100.0 (0.0)
subtype5 4 100.0 (0.0)
subtype6 3 93.3 (11.5)

Figure S38.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S44.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 189 153 112
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 1.29e-07 (logrank test), Q value = 1.1e-05

Table S45.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 454 156 0.1 - 120.6 (36.4)
subtype1 189 43 0.5 - 101.1 (37.1)
subtype2 153 51 0.2 - 120.6 (38.7)
subtype3 112 62 0.1 - 112.8 (26.4)

Figure S39.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00927 (ANOVA), Q value = 0.44

Table S46.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 453 60.4 (12.3)
subtype1 189 58.4 (12.7)
subtype2 152 62.3 (12.3)
subtype3 112 61.3 (11.1)

Figure S40.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 2e-06 (Chi-square test), Q value = 0.00016

Table S47.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 219 44 115 76
subtype1 118 16 36 19
subtype2 65 17 47 24
subtype3 36 11 32 33

Figure S41.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 4.03e-07 (Chi-square test), Q value = 3.4e-05

Table S48.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 224 54 165 11
subtype1 120 19 48 2
subtype2 67 20 65 1
subtype3 37 15 52 8

Figure S42.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.241 (Fisher's exact test), Q value = 1

Table S49.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 208 16
subtype1 72 4
subtype2 76 4
subtype3 60 8

Figure S43.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.000108 (Fisher's exact test), Q value = 0.0079

Table S50.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 379 75
subtype1 170 19
subtype2 130 23
subtype3 79 33

Figure S44.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.571 (Fisher's exact test), Q value = 1

Table S51.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 68 121
subtype2 47 106
subtype3 36 76

Figure S45.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0463 (ANOVA), Q value = 1

Table S52.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 93.5 (7.7)
subtype1 17 96.5 (4.9)
subtype2 10 89.0 (8.8)
subtype3 7 92.9 (9.5)

Figure S46.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S53.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 205 186 106
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 2.39e-07 (logrank test), Q value = 2.1e-05

Table S54.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 497 162 0.1 - 120.6 (36.5)
subtype1 205 43 0.2 - 120.6 (37.5)
subtype2 186 88 0.2 - 109.9 (31.2)
subtype3 106 31 0.1 - 112.8 (37.2)

Figure S47.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.115 (ANOVA), Q value = 1

Table S55.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 496 60.7 (12.2)
subtype1 204 61.6 (12.1)
subtype2 186 61.0 (11.8)
subtype3 106 58.6 (13.0)

Figure S48.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 2.61e-07 (Chi-square test), Q value = 2.2e-05

Table S56.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 240 54 124 79
subtype1 121 21 40 23
subtype2 57 21 64 44
subtype3 62 12 20 12

Figure S49.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 3.83e-06 (Chi-square test), Q value = 3e-04

Table S57.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 245 65 176 11
subtype1 121 24 58 2
subtype2 61 28 91 6
subtype3 63 13 27 3

Figure S50.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0131 (Fisher's exact test), Q value = 0.59

Table S58.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 233 17
subtype1 98 2
subtype2 86 12
subtype3 49 3

Figure S51.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0011 (Fisher's exact test), Q value = 0.067

Table S59.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 419 78
subtype1 182 23
subtype2 142 44
subtype3 95 11

Figure S52.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 4.91e-05 (Fisher's exact test), Q value = 0.0037

Table S60.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 170 327
subtype1 92 113
subtype2 44 142
subtype3 34 72

Figure S53.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.656 (ANOVA), Q value = 1

Table S61.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 35 90.9 (17.6)
subtype1 14 91.4 (8.6)
subtype2 13 87.7 (27.1)
subtype3 8 95.0 (7.6)

Figure S54.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S62.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 215 202 80
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 1.23e-08 (logrank test), Q value = 1.1e-06

Table S63.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 497 162 0.1 - 120.6 (36.5)
subtype1 215 46 0.2 - 120.6 (38.4)
subtype2 202 98 0.1 - 109.9 (30.6)
subtype3 80 18 0.4 - 112.8 (37.0)

Figure S55.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.121 (ANOVA), Q value = 1

Table S64.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 496 60.7 (12.2)
subtype1 214 61.5 (12.1)
subtype2 202 60.9 (11.6)
subtype3 80 58.2 (13.8)

Figure S56.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.75e-11 (Chi-square test), Q value = 1.6e-09

Table S65.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 240 54 124 79
subtype1 123 23 46 23
subtype2 59 23 69 51
subtype3 58 8 9 5

Figure S57.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 9.44e-11 (Chi-square test), Q value = 8.6e-09

Table S66.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 245 65 176 11
subtype1 123 28 63 1
subtype2 63 29 101 9
subtype3 59 8 12 1

Figure S58.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.00285 (Fisher's exact test), Q value = 0.17

Table S67.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 233 17
subtype1 101 1
subtype2 97 13
subtype3 35 3

Figure S59.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 5.51e-05 (Fisher's exact test), Q value = 0.0041

Table S68.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 419 78
subtype1 191 24
subtype2 153 49
subtype3 75 5

Figure S60.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00013 (Fisher's exact test), Q value = 0.0094

Table S69.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 170 327
subtype1 95 120
subtype2 50 152
subtype3 25 55

Figure S61.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.456 (ANOVA), Q value = 1

Table S70.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 35 90.9 (17.6)
subtype1 13 92.3 (6.0)
subtype2 12 85.8 (28.1)
subtype3 10 95.0 (9.7)

Figure S62.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S71.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 118 203 161
'MIRSEQ CNMF' versus 'Time to Death'

P value = 4.49e-06 (logrank test), Q value = 0.00035

Table S72.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 482 161 0.1 - 120.6 (36.5)
subtype1 118 34 0.1 - 112.8 (37.2)
subtype2 203 49 0.2 - 120.6 (37.2)
subtype3 161 78 0.2 - 94.0 (31.8)

Figure S63.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0386 (ANOVA), Q value = 1

Table S73.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 482 60.6 (12.2)
subtype1 118 58.7 (12.3)
subtype2 203 62.1 (12.2)
subtype3 161 60.0 (11.8)

Figure S64.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000131 (Chi-square test), Q value = 0.0094

Table S74.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 228 52 124 78
subtype1 67 11 30 10
subtype2 109 21 45 28
subtype3 52 20 49 40

Figure S65.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.000363 (Chi-square test), Q value = 0.025

Table S75.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 233 63 175 11
subtype1 69 11 34 4
subtype2 110 27 64 2
subtype3 54 25 77 5

Figure S66.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.0057 (Fisher's exact test), Q value = 0.31

Table S76.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 222 18
subtype1 56 3
subtype2 90 2
subtype3 76 13

Figure S67.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.000328 (Fisher's exact test), Q value = 0.023

Table S77.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 405 77
subtype1 109 9
subtype2 175 28
subtype3 121 40

Figure S68.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0056 (Fisher's exact test), Q value = 0.31

Table S78.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 163 319
subtype1 38 80
subtype2 84 119
subtype3 41 120

Figure S69.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.149 (ANOVA), Q value = 1

Table S79.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 9 95.6 (7.3)
subtype2 16 91.9 (8.3)
subtype3 11 77.3 (38.8)

Figure S70.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S80.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 261 42 179
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0162 (logrank test), Q value = 0.7

Table S81.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 482 161 0.1 - 120.6 (36.5)
subtype1 261 72 0.2 - 120.6 (36.3)
subtype2 42 16 0.5 - 93.0 (32.7)
subtype3 179 73 0.1 - 112.8 (37.0)

Figure S71.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0418 (ANOVA), Q value = 1

Table S82.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 482 60.6 (12.2)
subtype1 261 61.8 (12.3)
subtype2 42 57.7 (11.1)
subtype3 179 59.5 (12.1)

Figure S72.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0967 (Chi-square test), Q value = 1

Table S83.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 228 52 124 78
subtype1 132 28 62 39
subtype2 18 9 7 8
subtype3 78 15 55 31

Figure S73.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.00808 (Chi-square test), Q value = 0.4

Table S84.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 233 63 175 11
subtype1 135 35 89 2
subtype2 18 11 12 1
subtype3 80 17 74 8

Figure S74.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.0231 (Fisher's exact test), Q value = 0.9

Table S85.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 222 18
subtype1 119 4
subtype2 22 3
subtype3 81 11

Figure S75.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.685 (Fisher's exact test), Q value = 1

Table S86.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 405 77
subtype1 222 39
subtype2 34 8
subtype3 149 30

Figure S76.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.648 (Fisher's exact test), Q value = 1

Table S87.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 163 319
subtype1 93 168
subtype2 14 28
subtype3 56 123

Figure S77.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.878 (ANOVA), Q value = 1

Table S88.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 19 87.4 (22.8)
subtype2 2 95.0 (7.1)
subtype3 15 88.7 (25.3)

Figure S78.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S89.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 51 94 75
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.000565 (logrank test), Q value = 0.037

Table S90.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 220 67 0.2 - 120.6 (38.5)
subtype1 51 15 3.9 - 112.8 (35.2)
subtype2 94 18 0.2 - 120.6 (45.3)
subtype3 75 34 0.2 - 93.0 (36.2)

Figure S79.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.258 (ANOVA), Q value = 1

Table S91.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 220 59.4 (12.7)
subtype1 51 57.7 (12.6)
subtype2 94 61.0 (12.3)
subtype3 75 58.7 (13.2)

Figure S80.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.05 (Chi-square test), Q value = 1

Table S92.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 106 25 47 42
subtype1 32 2 8 9
subtype2 47 13 21 13
subtype3 27 10 18 20

Figure S81.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0178 (Chi-square test), Q value = 0.73

Table S93.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 109 28 77 6
subtype1 33 2 13 3
subtype2 47 16 30 1
subtype3 29 10 34 2

Figure S82.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.177 (Fisher's exact test), Q value = 1

Table S94.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 96 8
subtype1 17 2
subtype2 43 1
subtype3 36 5

Figure S83.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.105 (Fisher's exact test), Q value = 1

Table S95.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 181 39
subtype1 43 8
subtype2 82 12
subtype3 56 19

Figure S84.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.000559 (Fisher's exact test), Q value = 0.037

Table S96.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 65 155
subtype1 15 36
subtype2 39 55
subtype3 11 64

Figure S85.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.288 (ANOVA), Q value = 1

Table S97.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 30 93.3 (8.0)
subtype1 8 96.2 (7.4)
subtype2 13 90.8 (8.6)
subtype3 9 94.4 (7.3)

Figure S86.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S98.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 86 67 67
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.000808 (logrank test), Q value = 0.052

Table S99.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 220 67 0.2 - 120.6 (38.5)
subtype1 86 17 0.4 - 120.6 (47.3)
subtype2 67 29 0.2 - 93.0 (36.2)
subtype3 67 21 0.5 - 112.8 (29.9)

Figure S87.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.328 (ANOVA), Q value = 1

Table S100.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 220 59.4 (12.7)
subtype1 86 61.0 (12.3)
subtype2 67 58.9 (12.4)
subtype3 67 58.0 (13.3)

Figure S88.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0158 (Chi-square test), Q value = 0.69

Table S101.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 106 25 47 42
subtype1 45 11 21 9
subtype2 23 7 15 22
subtype3 38 7 11 11

Figure S89.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0178 (Chi-square test), Q value = 0.73

Table S102.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 109 28 77 6
subtype1 46 13 26 1
subtype2 24 8 34 1
subtype3 39 7 17 4

Figure S90.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.655 (Fisher's exact test), Q value = 1

Table S103.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 96 8
subtype1 34 2
subtype2 31 2
subtype3 31 4

Figure S91.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.00295 (Fisher's exact test), Q value = 0.17

Table S104.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 181 39
subtype1 77 9
subtype2 46 21
subtype3 58 9

Figure S92.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0164 (Fisher's exact test), Q value = 0.7

Table S105.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 65 155
subtype1 35 51
subtype2 16 51
subtype3 14 53

Figure S93.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.272 (ANOVA), Q value = 1

Table S106.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 30 93.3 (8.0)
subtype1 11 95.5 (5.2)
subtype2 10 90.0 (10.5)
subtype3 9 94.4 (7.3)

Figure S94.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Methods & Data
Input

  • Cluster data file = KIRC-TP.mergedcluster.txt

  • Clinical data file = KIRC-TP.merged_data.txt

  • Number of patients = 503

  • Number of clustering approaches = 12

  • Number of selected clinical features = 9

  • Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
Copyright © 2014 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle